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Prediction of flyrock distance induced by blasting using particle swarm optimization and multiple regression analysis: an engineering perspective

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ACTA GEOPHYSICA 2024年第1期72卷 287-301页

作者： Chen, Yong Wang, Minghua Yin, Heng Zhang, Tianbao China Construct Third Engn Bur Infrastruct Constru Gaoxin Rd 66 Wuhan 430070 Peoples R China China Shandong Int Econ & Tech Cooperat Corp Jinan Peoples R China

Flyrock is one of the major safety hazards induced by blasting operations. However, few studies were for predicting blasting-induced flyrock distance from the perspective of engineers. The present paper attempts to provide an engineer-friendly equation predicting blasting-induced flyrock distance. Data used in the present study contains s seven blasting parameters including borehole diameter, blasthole length, powder factor, stemming length, maximum charge per delay, burden, and flyrock distance is obtained. Data is inputted into Random Forest for feature selection. The selected features are formulated as two candidate equations, including multiple Linear regression (MLR) equation and multiple Nonlinear regression (MNR) equation. Those two candidates are respectively referred by Particle Swarm Optimization for searching optimum values for the coefficients of selected features. It is proved that MLR equation has better accuracy. MLR equation is compared with two empirical equations and the MLR equation based on least squares method. It is found that the coefficient of correlation of the proposed MLR equation reaches 0.918, which is the highest compared with the scores of other three equations. The present study utilizes feature selection process to screen inputs, which effectively excludes irrelevant parameters from being considered. Plus the contribution of Particle Swarm Optimization, the accuracy of the obtained equation can be guaranteed.

关键词： Blasting Flyrock distance Random forest Particle swarm optimization multiple regression analysis

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Modelling of the rheological behaviour of aluminium alloys in multistep hot deformation using the multiple regression analysis and artificial neural network techniques

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JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2006年第1-3期177卷 323-326页

作者： Bruni, C. Forcellese, A. Gabrielli, E. Simoncini, M. Polytech Univ Marche Dept Mech I-60131 Ancona Italy

Artificial neural network and multiple regression analysis techniques were applied in modelling the rheological behaviour of AA 6082 alummium alloy under multistep hot deformation conditions. To this end, multistage torsion tests were carried out in order to obtain the experimental data to be used in the development of the predictive models. The envelope curves predicted by both the ANN- and MRA-based models have shown an excellent fit, in terms of curve shape and stress level, with the experimental ones obtained under the same process conditions, even if the ANN based model has provided the best predictive capability. (c) 2006 Elsevier B.V. All rights reserved.

关键词： multistep deformation test artificial neural network multiple regression analysis flow modelling

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Application of multiple regression analysis in optimization of metronidazole-chitosan nanoparticles

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JOURNAL OF POLYMER RESEARCH 2019年第8期26卷 1-14页

作者： Sabbagh, Hazem Abdul Kader Abudayeh, Zead Abudoleh, Suha Mujahed Alkrad, Jamal Alyousef Hussein, Mohd Zobir Hussein-Al-Ali, Samer Hasan Isra Univ Fac Pharm POB 22 Amman 11622 Jordan Univ Putra Malaysia Inst Adv Technol ITMA Mat Synth & Characterizat Lab Seri Kembangan Malaysia

The current work aims to developing MET-CSNPs nanocomposites as drug delivery system. The nanocomposites were prepared by ionic interactions method and optimized using multiple regression analysis. Independent variables included chitosan concentration (CS), tri poly phosphate concentration (TPP) and metronidazole concentration (MET);while dependent variables were percentage loading drug (LE), zeta potential and zeta size. Prepared nanocomposites were characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), thermal gravimetric analysis (TGA), scanning electron microscope (SEM) and in vitro drug release studies. TGA, FTIR and XRD studies indicated the presence of drug into final nanocomposites. In vitro drug release from nanocomposites was carried out and showed that the release rate of MET from the MET-CSNPs nanocomposites was very slow. These results indicate extended release of the drug from its respective nanocomposites, and therefore these nanocomposites have good potential to be used as extended-release formulation of the drugs.

关键词： Metronidazole Chitosan nanoparticles multiple regression analysis Optimization

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A critical evaluation in analysing the influence of data analytics in enhancing supply chain management process through multiple regression analysis

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年第6期14卷 2080-2087页

作者： Mishra, Hari Govind Ratnesh, Kumar Tongkachok, Korakod Alanya-Beltran, Joel Kapila, Dhiraj Shri Mata Vaishno Devi Univ Sch Business Katra Jammu & Kashmir India Dewan Inst Management Studies Management Dept NH 58 Bypass Rd Partapur Meerut Uttar Pradesh India Thaksin Univ Fac Law Songkhla Thailand Univ Tecnol Peru Elect Dept Lima Peru Lovely Profess Univ Dept Comp Sci & Engn Phagwara India

This research analyses the influence of data analytics in enhancing the supply chain management process. From the global perspective, companies are focusing to remain competitive and foster growth by controlling the cost. In a typical supply chain management (SCM), the factors like capacity management, demand and expenses are regarded as recognized constraints. However, in the reality, there are uncertainties revolving around the overall consumer demand, risk involved in transportation, lead time differences and other aspects. The demand uncertainties tend to impact the SC performance in a wider span;hence companies tend to apply data analytics as a unique tool to forecast the demand, analyse the risk aspects and frame strategies to reduce the lead time. Hence, this study will enable in analysing the nature of impact which data analytics influences in supporting the SC process in the organisation. Major theme of the paper is intended to apprehend the critical influence of the big data analytics towards the supply chain management in selected companies in Europe, the researchers intends to measure the critical drivers of BDA in enhancing the SCM process and thereby support in realising the goals of the organisation. The researchers has collated data from 135 managers from the supply chain process in 15 different companies from Europe, the study tries to apply multiple regression analysis through SPSS and Structural equation modelling through partial least squares modelling was used to test the hypothesis. The final results obtained states that the data analytics tend to possess positive influence on the supply chain management process, supports the management in reducing the enhancing supplier relationship and enable in creating better supplier network design. This paper intends to provide clear and concise aspect on the current overview of literature related to data analytics and its effect on supply chain management process. It also reveals the theoretical aspects

关键词： Data analytics Supply chain management multiple regression analysis Consumer demand forecasting Network design Suppler relationship Enhanced visbility

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Prediction of column ozone concentrations using multiple regression analysis and principal component analysis techniques: A case study in peninsular Malaysia

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ATMOSPHERIC POLLUTION RESEARCH 2016年第3期7卷 533-546页

作者： Tan, Kok Chooi Lim, Hwee San Jafri, Mohd Zubir Mat Univ Sains Malaysia Sch Phys George Town 11800 Malaysia

The aim of this study is to develop new algorithms of the column ozone (O-3) in Peninsular Malaysia using statistical methods. Four regression equations, denoted as O-3 NEM, O-3 SWM, (PCA1) O-3 NEM season, and (PCA2) O-3 SWM season, were developed. multiple regression analysis (MRA) and principal component analysis (PCA) methods were utilized to achieve the objectives of the study. MRA was used to generate regression equations for O-3 NEM and O-3 SWM, whereas a combination of the MRA and PCA methods were used to generate regression equations for PCA1 and PCA2. The results of the best regression equations for the column O-3 through MRA by using four of the independent variables were highly correlated (R = 0.811 for SWM, R = 0.803 for NEM) for the six-year (2003-2008) data. However, the result of fitting the best equations for the O-3 data using four of the independent variables gave approximately the same R values (approximate to 0.83) for both the NEM and SWM seasons using the combined MRA and PCA methods. The common variables that appeared in both regression equations were H2O vapor and NO2. This result was expected because NO2 is a precursor of O-3. The correlation coefficients (R) of the validation for the NEM and SWM seasons were 0.877-0.888 and 0.837-0.896, respectively. These statistical values indicated a very good agreement between the monthly predicted and observed O-3 for Peninsular Malaysia. Copyright (C) 2016 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Ozone multiple regression analysis Principal component analysis Peninsular Malaysia Correlation coefficient

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Evidence that the relations between novelty-induced activity, locomotor stimulation and place preference induced by cocaine qualitatively depend upon the dose: a multiple regression analysis in inbred C57BL/6J mice

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BEHAVIOURAL BRAIN RESEARCH 2005年第2期158卷 201-210页

作者： Brabant, C Quertemont, E Tirelli, E Univ Liege Dept Sci Cognit Unite Neurosci Comportementales & Psychopharm Exp B-4000 Liege Belgium

It has been speculated that an individual's response to novelty is a reliable predictor of its vulnerability to develop addiction. However, the relationships between response to novelty and the development of drug-induced conditioned place preference are still unclear. The present study investigates the relationships between locomotor responses to novelty, cocaine-induced locomotor stimulation and conditioned place preference in C57BL/6J mice with multiple regression analyses. Four groups of mice receiving saline, 4, 8 or 12 mg/kg cocaine (i.p.) were submitted to an 8-day unbiased counterbalanced place conditioning protocol. Levels of locomotion on the pre-conditioning session were used as a score of locomotor response to a novel environment. The locomotor activity on the first cocaine-pairing session of the conditioning procedure served as a measure of the locomotion-activating response to a single injection of cocaine. Cocaine-induced dose-dependent locomotor stimulant effects and a significant place preference at all tested doses. A positive correlation was found between the locomotor responses to novelty and the locomotor stimulant effects of cocaine, but was significant only for the highest dose of cocaine (12 mg/kg). In contrast, there was a negative correlation between the locomotor response to novelty and the conditioned place preference induced by 4 mg/kg cocaine. Finally, the locomotor stimulant effects of cocaine do not correlate with cocaine-induced conditioned place preference at any tested dose of cocaine. The relationships between locomotor response to novelty and both cocaine-induced stimulant and rewarding effects can be differentially affected by the dose in inbred C57BL/6J mice. (C) 2004 Elsevier B.V. All rights reserved.

关键词： cocaine C57BL/6J mice locomotion conditioned place preference novelty multiple regression analysis

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Application of multiple regression analysis in projecting the water demand for the City of Cape Town

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WATER PRACTICE AND TECHNOLOGY 2018年第3期13卷 705-711页

作者： Lawens, M. Mutsvangwa, C. Univ Appl Sci Mainz Dept Civil Engn Mainz Germany Cape Peninsula Univ Technol Dept Civil Engn & Surveying Cape Town South Africa

Historically, the city of Cape Town has been affected by water shortages and it can be assumed that the situation will be exacerbated in the coming decades by a growing population, economic development and climatic changes as additional stress factors. In order to defuse the situation, the city of Cape Town has commissioned various feasibility studies concerning the implementation of alternative water sources, with as yet unpublished conclusions. Since sustainable water resource planning requires a comprehensive understanding of the water demand, the objective of this study was to predict the future demand by the city of Cape Town by analysing its significant drivers. For this purpose, a linear multiple regression analysis was applied on parameters which influence water demand, namely: population, economy, water losses and water restrictions. In order to establish the linear multiple regression model and its regression coefficients, historical data was used for the period 2001 to 2012. The result of the regression analysis showed that the water demand of the city of Cape Town is only decisively influenced by population and water losses. In addition, the model indicated that a new source would be required by 2021. Thus, water conservation and water supply strategies can be adapted accordingly to ultimately enable a sustainable management of the water sources in the city of Cape Town.

关键词： city of Cape Town multiple regression analysis water deficit water demand

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multiple regression analysis of Catalytic Dehydrogenation of Isopropanol in a Chemical Heat Pump System

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CHEMICAL ENGINEERING & TECHNOLOGY 2015年第3期38卷 399-408页

作者： Mert, Mehmet Selcuk Salt, Inci Karaca, Fatma Mert, Hatice Hande Bolat, Esen Yalova Univ Energy Syst Engn Dept TR-77100 Yalova Turkey Yildiz Tech Univ Dept Chem Engn Istanbul Turkey Marmara Univ Dept Chem Engn Istanbul Turkey Yalova Univ Chem & Proc Engn Dept Yalova Turkey

Chemical heat pump is a clean technology developed to upgrade the low-level thermal energy to upper levels and to store energy without losses caused by temperature differences. multiple regression analysis of catalytic dehydrogenation of isopropanol was performed. The endothermic dehydrogenation of isopropanol was carried out under continuous boiling and refluxing conditions in order to study the enhancement effects of the presence of an alkaline compound and different types of catalysts at various concentrations in the reaction medium on the evolution rate of hydrogen. The factorial experimental design method was applied to understand better the coupled influences of both catalyst and alkaline additive concentrations to discuss and evaluate statistically the results for different catalysts and to develop the related models.

关键词： Catalytic dehydrogenation Chemical heat pump multiple regression analysis

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Early cost estimation models based on multiple regression analysis for road and railway tunnel projects

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ARABIAN JOURNAL OF GEOSCIENCES 2021年第11期14卷 1-10页

作者： Ahmed, Chro Univ Sulaimani Coll Engn Civil Engn Dept Kirkuk Rd Sulaimani Kurdistan Iraq

Estimates of a tunnel construction cost are among the most critical tasks during the planning stage of both road and railway projects to justify the project and allow a valid comparison between alternative solutions and perform reliable "what if" scenarios relative to the tunnel diameter and length. Numerous factors influence the tunnel construction cost, and very little information on these factors is available at the early stage of project planning. Developing an accurate cost estimate is therefore very difficult at this stage, and thus, a very limited number of cost models are available for this purpose. This paper develops early parametric cost estimating models for road and railway tunnels in the planning stage of a project based upon the application of multiple regression analysis on 25 constructed projects located in Western European countries. The developed models incorporate not only tunnel length and diameter but also the type of tunneling methods (mechanized and conventional), which are largely affected by geological conditions. The results showed high correlation coefficients (R-2) of 0.968 and 0.79 for mechanized and conventional tunneling models respectively. In addition, the results of the developed models were compared against actual costs to assess their accuracy and robustness. The developed models achieved cost estimation accuracy over 75%, indicating that the models fit for their purpose and lead to fairly accurate cost estimates of road and railway tunnels.

关键词： Early cost estimation Tunneling methods multiple regression analysis Actual cost Predicted cost

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Clinical factors influencing implant positioning by guided surgery using a nonmetal sleeve template in the partially edentulous ridge: multiple regression analysis of a prospective cohort

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CLINICAL ORAL IMPLANTS RESEARCH 2020年第12期31卷 1187-1198页

作者： Park, Jin-Young Song, Young Woo Park, Seung-Hyun Kim, Jang-Hyun Park, Ji-Man Lee, Jung-Seok Yonsei Univ Res Inst Periodontal Regenerat Dept Periodontol Coll Dent 50 Yonsei Ro Seoul 03722 South Korea Yonsei Univ Dept Prosthodont Coll Dent Seoul South Korea

Objective To determine the positional accuracy of implants placed with a three-dimensionally printed template having nonmetal sleeves and to determine the contributing factors to observed deviations. Materials and Methods One hundred and eighty-seven implants placed in 72 patients were analyzed. Presurgical intraoral scans and cone-beam computed tomography images obtained before and after surgery were superimposed, and vertical, angular, platform, and apex deviations were measured between the virtually planned and actually placed positions. A multiple linear regression model was designed for identifying the contributing factors. Statistical significance was set atp < .05, with Bonferroni correction if necessary (p < .0167). Results A total of 187 implants demonstrated deviations of 0.65 [0.56, 0.75] mm (mean [95% confidence interval]) vertically, 3.59 degrees [3.30 degrees, 3.89 degrees] angularly, 1.16 [1.04, 1.28] mm at platform, and 1.50 [1.36, 1.65] mm at apex. Implants placed in the mandible showed larger angular, platform, and apex deviations compared with those in the maxilla (p = .049,p = .014 andp = .003, respectively). Implants placed at the third or fourth nearest sites from the most-distal tooth had larger deviations than those placed at the first or second nearest sites, in vertical, platform, and apical aspects (p = .015,p = .011 andp = .018, respectively). This was only applicable to free-ending-supported templates (p < .0167), and anchor pin-supported free-ending templates (p < .0167). Conclusion Using a three-dimensionally printed surgical template with a nonmetal sleeve in the partial edentulous ridge resulted in larger deviations in implants placed in the mandible or distal free-end third or fourth nearest site.

关键词： accuracy clinical research guided implant surgery multiple regression analysis nonmetal sleeve template

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